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stations-classification-200m.Rmd
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---
output: github_document
---
The aim of the code in this document is to classify stations according to the demographics of people living near them.
# Setup
```{r}
library(lchs)
tmap_mode("view")
knitr::opts_chunk$set(eval = TRUE)
```
```{r, eval=FALSE}
install.packages("showtext")
library(showtext)
font_add(family = "Avenir Book", regular = "~/hd/backups/Avenir Book/Avenir Book.ttf")
font_add_google("Gochi Hand", "gochi")
font_add_google("Schoolbell", "bell")
showtext_auto()
x11()
hist(rnorm(1000), breaks = 30, col = "steelblue", border = "white",
main = "", xlab = "", ylab = "")
title("Histogram of Normal Random Numbers", family = "bell", cex.main = 2)
title(ylab = "Frequency", family = "gochi", cex.lab = 2)
text(2, 70, "N = 1000", family = "bell", cex = 2.5)
```
# Input data
Get, plot and preprocess stations data
```{r, eval=TRUE}
# from bikedata stations table
stations_region = readd(stations_region)
stations = readd(stations_yearly)
stations$id = as.character(stations$ucl_id)
stations_2019 = stations %>% filter(year == 2019)
qtm(stations_region) +
qtm(stations_2019)
```
# Change in income decile of LSOA over time
```{r}
# see code that generates this below
lsoa = read_sf("lsoa_bikeshare.geojson")
plot(lsoa)
lsoa = lsoa %>% select(`Income decile` = Income.Decile..where.1.is.most.deprived.10..of.LSOAs.)
plot(lsoa)
stations_yearly_lsoa = st_join(stations, lsoa)
saveRDS(stations_yearly_lsoa, "stations_yearly_lsoa.Rds")
# counts
stations_yearly_lsoa %>%
filter(year %in% c(2010, 2012, 2014, 2019)) %>%
ggplot(aes(`Income decile`)) +
geom_bar(fill = "blue") +
facet_wrap(~year) +
ggthemes::theme_clean(base_family = "gochi")
# props
# source("get-imd-data.R")
source("code/old-code/get-imd-data.R") # if running from root dir
stations_yearly_lsoa %>%
filter(year %in% c(2010, 2012, 2014, 2019)) %>%
# mutate(n_year = sum(year == year)) %>% View()
ggplot(aes(`Income decile`)) +
geom_bar(aes(y=..count../sum(..count..)), fill = "blue") +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~year) +
ggthemes::theme_clean()
stations_yearly_lsoa %>%
filter(year %in% c(2010, 2012, 2014, 2019)) %>%
# mutate(n_year = sum(year == year)) %>% View()
ggplot(aes(`Income decile`)) +
geom_bar(data = lsoa_lnd_joined, aes(`Income decile`, y=..count../sum(..count..)), fill = "grey", width = .95) +
geom_bar(aes(y=..count../sum(..count..)), fill = "blue", width = .60, alpha = 0.7) +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~year)
stations_yearly_4 = stations_yearly_lsoa %>%
filter(year %in% c(2010, 2012, 2014, 2019)) %>%
sf::st_drop_geometry() %>%
group_by(year) %>%
mutate(n_year = n()) %>%
ungroup() %>%
group_by(year, `Income decile`) %>%
summarise(
n_decile_year = n(),
n_year = unique(n_year),
s = diff(range(n_year)),
) %>%
mutate(p = n_decile_year / n_year) %>%
group_by(year) %>%
mutate(t = sum(p))
# stations_yearly_4 = stations_yearly_4 %>%
# mutate(`Income decile` = formatC(x = `Income decile`, width = 2, flag = "0"))
stations_yearly_4 = stations_yearly_4 %>%
mutate(`Income decile` = as.numeric(x = `Income decile`))
stations_yearly_4 %>% ggplot() +
geom_bar(data = lsoa_lnd_joined, aes(`Income decile`, y=..count../sum(..count..) * 4),
fill = "#bdbdbd", width = .95) +
geom_bar(aes(`Income decile`, p), stat = "identity", fill = "#3182bd", width = .60, alpha = 0.7) +
scale_y_continuous(labels = scales::percent_format()) +
scale_x_continuous(breaks = 1:10) +
facet_wrap(~year) +
ylab("Proportion of docking stations in each income decile")
# 1 row version
stations_yearly_4 %>% ggplot() +
geom_bar(data = lsoa_lnd_joined, aes(`Income decile`, y=..count../sum(..count..) * 4),
fill = "#bdbdbd", width = .95) +
geom_bar(aes(`Income decile`, p), stat = "identity", fill = "#3182bd", width = .60, alpha = 0.7) +
scale_y_continuous(labels = scales::percent_format()) +
scale_x_continuous(breaks = 1:10) +
facet_wrap(~year, nrow = 1) +
ylab("% stations in each income decile") +
theme_minimal()
# theme_set(ggthemes::theme_clean(base_family = "Avenir Book"))
# theme(axis.title=element_blank(), panel.border = element_blank(),
# strip.text.y = element_text(angle=0))
ggsave("figures/stations-imd-facet-4-grey.png", width = 6, height = 3)
magick::image_read("figures/stations-imd-facet-4-grey.png")
# calculate scores
stations_income_yearly_df = stations_yearly_lsoa %>%
group_by(year) %>%
summarise(
median_income_decile = median(`Income decile`),
mean_income_decile = mean(`Income decile`),
sd_income_decile = sd(`Income decile`)
) %>%
st_drop_geometry()
stations_income_yearly_df
# ggsave("figures/stations-imd-facet-4.png")
```
# Join with stations data
```{r}
# trips_df = fst::read_fst("trips_df_all.fst")
trips_df = readd(trips_df)
stations = readd(stations)
stations = st_join(stations, lsoa)
stations$years_in_operation = as.numeric(max(trips_df$start_time) - stations$created_dt + 100) / 365
trips_by_origin_station = trips_df %>%
group_by(id = start_station_id) %>%
summarise(total_n_trips_start = n())
trips_by_origin_station$id = as.character(trips_by_origin_station$id)
head(stations$ucl_id)
head(trips_by_origin_station$id)
stations = inner_join(stations %>% mutate(id = as.character(ucl_id)), trips_by_origin_station)
summary(stations$total_n_trips_start) # all stations there!
hist(stations$total_n_trips_start)
stations$trips_per_year = stations$total_n_trips_start / stations$years_in_operation
tm_shape(stations %>% select(operator_name, trips_per_year)) +
tm_dots(size = "trips_per_year", alpha = 0.5) + # incorrect labels - bug in tmap?
tm_scale_bar()
summary(stations$trips_per_year)
plot(stations$trips_per_year) # 500000 seems excessive
plot(stations$years_in_operation, stations$trips_per_year)
stations$years_in_operation[stations$trips_per_year > 50000 & stations$years_in_operation < 2] # all seem suspect
median(stations$years_in_operation)
# stations$years_in_operation[stations$trips_per_year > 50000 & stations$years_in_operation < 2 ] = median(stations$years_in_operation)
stations$years_in_operation[stations$trips_per_year > 50000 & stations$years_in_operation < 2 ] = NA
stations$trips_per_year = stations$total_n_trips_start / stations$years_in_operation
tm_shape(stations %>% select(operator_name, trips_per_year)) +
tm_dots(size = "trips_per_year", alpha = 0.5) + # incorrect labels - bug in tmap?
tm_scale_bar() # looks good!
tm_shape(stations %>% select(operator_name, total_n_trips_start)) +
tm_dots(size = "total_n_trips_start", alpha = 0.5) + # incorrect labels - bug in tmap?
tm_scale_bar()
# tm_shape(stations) + tm_markers(text = "operator_name")
mapview::mapview(stations)
```
Add additional usage stats
```{r, eval=FALSE}
library(lubridate)
am_peak_int <- interval(hms::as_hms("06:00:00"), hms::as_hms("09:59:59"))
pm_peak_int <- interval(hms::as_hms("16:00:00"), hms::as_hms("19:59:59"))
trips_am_peak = trips_df %>% filter(as.POSIXct(hms::as_hms(start_time)) %within% am_peak_int)
trips_pm_peak = trips_df %>% filter(as.POSIXct(hms::as_hms(start_time)) %within% pm_peak_int)
fst::write_fst(trips_am_peak, "trips_am_peak.fst")
fst::write_fst(trips_pm_peak, "trips_pm_peak.fst")
piggyback::pb_upload("trips_am_peak.fst")
piggyback::pb_upload("trips_pm_peak.fst")
```
## Starting from am/pm peak data
```{r}
trips_pm_peak = fst::read.fst("trips_pm_peak.fst")
trips_am_peak = fst::read.fst("trips_am_peak.fst")
# am
trips_by_origin_station = trips_am_peak %>%
group_by(id = start_station_id) %>%
summarise(total_n_am_peak = n())
trips_by_origin_station$id = as.character(trips_by_origin_station$id)
stations = inner_join(stations, trips_by_origin_station)
stations$trips_per_year_am = stations$total_n_am_peak / stations$years_in_operation
# pm
trips_by_origin_station = trips_pm_peak %>%
group_by(id = start_station_id) %>%
summarise(total_n_pm_peak = n())
trips_by_origin_station$id = as.character(trips_by_origin_station$id)
stations = inner_join(stations, trips_by_origin_station)
stations$trips_per_year_pm = stations$total_n_pm_peak / stations$years_in_operation
sf::write_sf(stations, "stations_clean_am_pm.geojson", delete_dsn = TRUE)
piggyback::pb_upload("stations_clean_am_pm.geojson")
```
```{r}
tmap_mode("plot")
m = tm_shape(stations %>% select(operator_name, trips_per_year, trips_per_year_am, trips_per_year_pm, years_in_operation)) +
tm_dots(size = c("trips_per_year_am", "trips_per_year_pm"),
col = "years_in_operation", palette = "Spectral", contrast = c(0, 0.8), alpha = 0.5, scale = 1.5, title.size = "") +
tm_layout(panel.show = TRUE,
panel.labels = c("AM (06:00 - 10:00)", "PM (16:00 - 20:00)"),
legend.outside = T, title = "N. trips/year") +
tm_facets(ncol = 2)
m
tmap_save(tm = m, "figures/map-am-pm-peaks.png")
```
Get OAC data (not evaluated)
```{r, eval=FALSE}
devtools::install_github("robinlovelace/ukboundaries")
# download.file("https://data.cdrc.ac.uk/dataset/68771b14-72aa-4ad7-99f3-0b8d1124cb1b/resource/8fff55da-6235-459c-b66d-017577b060d3/download/output-area-classification.zip", "output-area-classification.zip") # fails
oac = sf::read_sf("Output Area Classification/Shapefiles/2011_OAC.shp")
u_oas_cents = "https://opendata.arcgis.com/datasets/ba64f679c85f4563bfff7fad79ae57b1_0.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D"
u_oas_cents = ukboundaries::duraz(u_oas_cents)
oas_lnd = u_oas_cents %>% st_transform(4326)
oas_lnd = oas_lnd[stations_region, ] # works
oac_lnd = oac %>% filter(OA_SA %in% oas_lnd$oa11cd)
plot(oac_lnd["SPRGRP"])
# IMD data (at LSOA level)
# imd = readr::read_csv("https://opendata.arcgis.com/datasets/da3b33dd44d94f48a9628a3391957505_0.csv") # seems to be ranks...
# imd = readr::read_csv("https://data.cdrc.ac.uk/dataset/4d3a8738-38af-401c-8070-6be5d85b2f5e/resource/a4230484-9104-4903-b754-0d07a1278862/download/imd2015eng.csv") # navigate to that URL and manually download...
imd = readr::read_csv("imd2015eng.csv")
lsoa = ukboundaries::lsoa2011_simple
lsoa_lnd = lsoa[oas_lnd, ]
imd = imd %>%
rename(lsoa11cd = `LSOA code (2011)` )
lsoa_lnd = inner_join(lsoa_lnd, imd)
plot(lsoa_lnd["Index of Multiple Deprivation (IMD) Score"])
sf::write_sf(lsoa_lnd, "lsoa_bikeshare.geojson")
oas_lnd_imd = st_join(oas_lnd, lsoa_lnd)
plot(oas_lnd_imd["Index of Multiple Deprivation (IMD) Score"])
sf::write_sf(oas_lnd_imd, "oas_bikeshare_imd.geojson")
names(oas_lnd_imd) = snakecase::to_snake_case(names(oas_lnd_imd))
names(oas_lnd)
plot(oas_lnd_imd[7:20])
# sf::write_sf(oas_lnd_imd, "oas_bikeshare_imd_tidynames.geojson")
# piggyback::pb_upload("oas_bikeshare_imd_tidynames.geojson")
# piggyback::pb_upload("oas_bikeshare_imd.geojson")
# piggyback::pb_upload("lsoa_bikeshare.geojson")
```
# Classify docking stations
Based on number of nearby residential zones and IMD/income scores.
Initial method: by distance to docking stations (flaw with 300m: misses reality of walking that far, try 200m).
```{r}
oas = read_sf("oas_bikeshare_imd_tidynames.geojson")
if(!file.exists("sape21dt10hmid2018east.zip")) {
u_oas_pop = "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fcensusoutputareaestimatesintheeastregionofengland%2fmid2018sape21dt10h/sape21dt10hmid2018east.zip"
download.file(u_oas_pop, "sape21dt10hmid2018east.zip")
unzip("sape21dt10hmid2018east.zip")
}
# mid 2018 estimates: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/censusoutputareaestimatesintheeastregionofengland
# oas_pop = readxl::read_excel("SAPE21DT10h-mid-2018-coa-unformatted-syoa-estimates-east.xlsx", sheet = 4)
# oas_name = as.character(oas_pop[4, ])
# oas_pop = oas_pop %>% slice(5:nrow(oas_pop))
# names(oas_pop) = oas_name
# oas_pop_16plus = oas_pop %>% mutate(Population = rowSums(select(., 20:(ncol(oas_pop) - 1)))) %>%
# select(OA11CD, Population)
# see https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london
if(!file.exists("statistical-boundaries-london.zip")) {
download.file("https://data.london.gov.uk/download/statistical-gis-boundary-files-london/9ba8c833-6370-4b11-abdc-314aa020d5e0/statistical-gis-boundaries-london.zip", "statistical-boundaries-london.zip")
unzip("statistical-boundaries-london.zip")
}
oaps_all_london = sf::read_sf("statistical-gis-boundaries-london/ESRI/OA_2011_London_gen_MHW.shp")
plot(oaps_all_london$geometry)
oaps = oaps_all_london %>% st_transform(4326)
oaps = oaps[stations_region, ]
plot(oaps)
oaps = oaps %>% select(OA11CD, Populaton2011 = USUALRES)
plot(oaps)
stations = sf::read_sf("stations_clean_am_pm.geojson")
plot(stations["Income.decile"])
stations_200m_buffer = stations %>%
st_transform(27700) %>%
st_buffer(200) %>%
st_transform(4326)
mapview::mapview(stations_200m_buffer)
# aim: get to trips per person within 200m per docking station
# geofrabric method fails - see https://github.com/ITSLeeds/geofabric/issues/16
# lnd_osm = ukboundaries::duraz("http://download.geofabrik.de/europe/great-britain/england/greater-london-latest-free.shp.zip")
# lnd_osm = sf::read_sf("gis_osm_buildings_a_free_1.shp")
# lnd_osm_stations = lnd_osm[stations_region, ]
# saveRDS(lnd_osm_stations, "lnd_osm_stations.Rds")
# piggyback::pb_upload("lnd_osm_stations.Rds")
lnd_osm_stations = readRDS("lnd_osm_stations.Rds")
# plot(lnd_osm_stations$geometry) # slow to plot
lnd_osm_resi = lnd_osm_stations %>%
filter(str_detect(string = type, pattern = "house|resi|apartments|terrace|flats|council_flats") |
is.na(type))
plot(lnd_osm_resi$geometry, col = "red", border = "red", add = TRUE)
# idea: use building centroids and calculate n. people per building
lnd_osm_centroids = st_centroid(lnd_osm_resi)
table(lnd_osm_centroids$OA11CD) %>% head()
lnd_osm_centroids = st_join(lnd_osm_centroids, oaps)
# get number of buildings per zones
lnd_osm_centroids_with_count = lnd_osm_centroids %>% dplyr::add_count(OA11CD)
lnd_osm_centroids$n = lnd_osm_centroids_with_count$n
mapview::mapview(lnd_osm_centroids %>% filter(n == max(n)))
lnd_osm_centroids = lnd_osm_centroids %>% mutate(pop_per_building = Populaton2011 / n)
lnd_osm_near_stations = lnd_osm_centroids[stations_200m_buffer, ]
# mapview::mapview(stations_200m_buffer) + mapview::mapview(lnd_osm_near_stations)
plot(sf::st_geometry(lnd_osm_near_stations))
stations_pop = aggregate(lnd_osm_near_stations["pop_per_building"], stations_200m_buffer, sum)
stations_pop = stations_pop %>% rename(Population = pop_per_building)
stations_pop
stations = st_join(stations, stations_pop)
sum(stations_pop$Population, na.rm = TRUE)
sum(lnd_osm_centroids$pop_per_building, na.rm = TRUE)
plot(stations_pop)
# mapview::mapview(stations_pop) + mapview::mapview(lnd_osm_centroids)
saveRDS(stations, "stations_pop_200m.Rds")
piggyback::pb_upload("stations_pop.Rds")
# write_sf(stations, "stations_pop.geojson", delete_dsn = TRUE)
# plot result
m = tm_shape(oaps) + tm_fill("Populaton2011", palette = "Blues", title = "Population") +
tm_shape(lnd_osm_centroids) + tm_dots(col = "green", alpha = 0.2) +
tm_shape(lnd_osm_near_stations) + tm_dots(col = "red", alpha = 0.6) +
tm_shape(stations) + tm_dots(size = 0.07)
m
tmap_save(m, "figures/bikeshare-resi-buildings.png")
```
## Now use the population estimates to show AM peak usage (per year?)
```{r}
stations = readRDS("stations_pop_200m.Rds")
names(stations)
head(stations$)
stations = stations %>% rename(income_decile = Income.decile)
summary(stations$income_decile)
stations$trips_per_person_yr = stations$trips_per_year / stations$Population / stations$years_in_operation
stations$trips_per_person_yr_am = stations$trips_per_year_am / stations$Population
stations$trips_per_person_yr_pm = stations$trips_per_year_pm / stations$Population
summary(stations$trips_per_person_yr)
stations$trips_per_person_yr[stations$trips_per_person_yr > 5]
unique(stations$income_decile)
stations$income_decile = formatC(x = stations$income_decile, width = 2, flag = "0")
class(stations$income_decile) = "character"
# previously was ggbeeswarm::geom_beeswarm, replaced by geom_violin
theme_set(theme_minimal())
g1 = ggplot(stations %>% filter(! income_decile == "NA")) + geom_boxplot(aes(income_decile, trips_per_person_yr), draw_quantiles = c(0.25, 0.5, 0.75)) + scale_y_continuous(limits = c(0.1, 20)) + ylab("Number of trips per local resident")
g1
g2 = ggplot(stations %>% filter(! income_decile == "NA")) + geom_boxplot(aes(income_decile, trips_per_person_yr_am), draw_quantiles = c(0.25, 0.5, 0.75)) + scale_y_continuous(limits = c(0.1, 20)) + ylab("Number of trips per local resident (AM)") + xlab("Income decile")
g2
g3 = ggplot(stations %>% filter(! income_decile == "NA")) + geom_boxplot(aes(income_decile, trips_per_person_yr_pm), draw_quantiles = c(0.25, 0.5, 0.75)) + scale_y_continuous(limits = c(0.1, 20)) + ylab("Number of trips per local resident (PM)") + xlab("Income decile")
g3
library(patchwork)
p = g2 + g3 + plot_layout(ncol = 1)
p
ggsave("figures/income-decile-am-pm-boxplot-200m.png", p, width = 9, height = 7)
magick::image_read("figures/income-decile-am-pm-boxplot.png")
# ggplot(stations %>% filter(!is.na(income_decile))) + geom_boxplot(aes(group = income_decile, y = trips_per_person_yr)) + scale_x_discrete(labels = 1:10)
summary(stations$income_decile)
g = ggplot(stations) + geom_point(aes(Population, total_n_trips_start))
g
cor(stations$Population, stations$total_n_trips_start, use = "complete.obs")
cor(stations$Population, stations$trips_per_year_am, use = "complete.obs")
# postive correlation, expected
quantile(x = stations$trips_per_person_yr, probs = c(0.1, 0.5, 0.7, 0.9), na.rm = T)
# check deciles - are there really that many deprived oas in London?
# file.remove("stations_imd_pop.geojson")
write_sf(stations, "stations_imd_pop_200m.geojson", delete_dsn = TRUE)
piggyback::pb_upload("stations_imd_pop.geojson")
# hist(stations$imd)
```
## Exploration of residential zones
```{r, eval=FALSE}
tmap_mode("view")
tm_shape(stations) + tm_dots(col = "n_oas", size = 0.1)
tmap_mode("plot")
qtm(stations %>% filter(n_oas <= 2))
stations_residential = stations %>% filter(trips_per_person_yr_am > 1, n_oas >= 1)
tm_shape(stations_residential) + tm_dots()
stations_residential %>%
filter(trips_per_person_yr_am > 5) %>%
tm_shape() + tm_dots() # excludes rail station
nrow(stations_residential)
summary(stations_residential$trips_per_person_yr)
table(stations_residential$n_oas)
summary(stations_residential$Population)
# 0.16 with outliers removed
cor(stations_residential$Population, stations_residential$total_n_trips_start, use = "complete.obs") # positive cor
# write_sf(stations_residential, "stations_residential.geojson")
# piggyback::pb_upload("stations_residential.geojson")
tm_shape(stations) + tm_dots("grey") +
tm_shape(stations_residential) + tm_dots()
sf::write_sf(stations_residential, "stations_residential.geojson", delete_dsn = TRUE)
piggyback::pb_upload("stations_residential.geojson")
```
Subsetting only OD pairs with origins in the 'residential stations'.
```{r, eval=FALSE}
trips_df$id = as.character(trips_df$id)
stations_residential$id
unique(trips_df$start_station_id)
trips_df_originating_in_residential_zones = trips_df$start_station_id %in% stations_residential$id
summary(trips_df_originating_in_residential_zones)
sum(trips_df_originating_in_residential_zones) / nrow(trips_df) # 40% trips remain
trips_resi = trips_df %>%
filter(trips_df_originating_in_residential_zones)
summary(trips_resi$start_station_id %in% stations_residential$id)
summary(trips_resi$end_station_id %in% stations_residential$id)
fst::write.fst(trips_resi, "london_bike_hire_cleaned_residential.fst")
piggyback::pb_upload("london_bike_hire_cleaned_residential.fst")
# vroom::vroom_write(trips_resi, "london_bike_hire_cleaned_residential.csv.gz")
# piggyback::pb_upload("london_bike_hire_cleaned_residential.csv.gz")
```
# Quantifying inequality in provision, compared with randomly sampled points
How does the distribution of IMD scores associated with the stations compare with IMD scores associated with randomly sampled points?
```{r, eval=FALSE}
sdf = read_sf("sdf.geojson")
set.seed(1985)
random_points = st_sample(x = sdf[4, ], size = nrow(stations))
random_points_oas = aggregate(oas["index_of_multiple_deprivation_imd_score"], random_points, mean, join = j)
random_points_oas = aggregate(oas["index_of_multiple_deprivation_imd_score"], random_points, mean, join = j)
hist(random_points_oas$index_of_multiple_deprivation_imd_score)
hist(stations$imd)
```
```{r cleaning-old, eval=FALSE, echo=FALSE}
# from CDRC
piggyback::pb_download("stations_ucl_yearly.geojson")
piggyback::pb_download("stations2019.geojson")
plot(stations, add = TRUE)
length(unique(stations$ucl_id))
# cleaning stations data
stations = stations %>% ungroup() %>% rename(id = ucl_id) %>% mutate(id = as.character(id))
table(stations$year) # trust the 2012, 2014, 2015, 2016 ones
plot(stations$initial_size, stations$curr_size) # interesting contrast
# ids in bikedata but not ucl
(ids_in_bikedata_not_in_clean_ucl_data = stations_bikedata$id[! stations_bikedata$id %in% stations$id])
(ids_in_bikedata_not_in_ucl_data = stations_bikedata$id[! stations_bikedata$id %in% stations$id])
(ids_in_ucl_data_not_in_bikedata = stations$id[! stations$id %in% stations_bikedata$id])
```
```{r, eval=FALSE, echo=FALSE}
# out-takes
# old method
summary(st_area(oas))
plot(lsoa_lnd$geometry)
(sum(st_area(lsoa_lnd)) / nrow(oas))^0.5 # on average around 200 apart
sel_200 = st_is_within_distance(stations, oas, 200)
summary(lengths(sel_200)) # catches on average 5 areas
j = function(x, y) st_is_within_distance(x, y, dist = 200)
# stations_oas = aggregate(oas["index_of_multiple_deprivation_imd_score"], stations, weighted.mean, join = j, w = oas$total_population_mid_2012_excluding_prisoners) # fails - w not of right length
stations_oas = aggregate(oas["index_of_multiple_deprivation_imd_score"], stations, mean, join = j)
stations$imd = stations_oas$index_of_multiple_deprivation_imd_score
plot(stations["imd"])
summary(stations$imd)
plot(stations_oas) # they are the same
stations_pop = aggregate(oas["population_aged_16_59_mid_2012_excluding_prisoners"], stations, sum, join = j)
stations_n_oas = aggregate(oas["population_aged_16_59_mid_2012_excluding_prisoners"], stations, length, join = j)
stations_median_decile = aggregate(oas["index_of_multiple_deprivation_imd_decile_where_1_is_most_deprived_10_of_lso_as"], stations, median, join = j)
stations_median_income = aggregate(oas["income_decile_where_1_is_most_deprived_10_of_lso_as"], stations, median, join = j)
names(stations_n_oas)[1] = "n_oas_200m"
plot(stations_pop)
plot(stations_n_oas)
hist(stations_n_oas$n_oas_200m)
# <!-- Let's plot what just happened. Let's take station 1: -->
oas1 = oas[stations[1, ], , op = st_is_within_distance, dist = 200]
mapview::mapview(oas1) + mapview::mapview(stations[1, ])
# Note: The distance for 300m buffer looked far for the outer stations. Note: try with lower distance in future.
# For now, let's add the new aggregated values and save the result:
# stations$pop_centroid_within_200m = stations_pop$population_aged_16_59_mid_2012_excluding_prisoners
stations$n_oas = stations_n_oas$n_oas_200m
stations$imd_decile_median_where_1_is_most_deprived = stations_median_decile$index_of_multiple_deprivation_imd_decile_where_1_is_most_deprived_10_of_lso_as %>% round()
plot(stations$imd_decile_median_where_1_is_most_deprived)
stations$income_decile = stations_median_income$income_decile_where_1_is_most_deprived_10_of_lso_as %>% round()
```